Interpretive Summary: Many microbial source tracking methods rely upon the use of specific marker genes or certain fecal indicator bacteria to identify sources of fecal contamination. However, these methods often fail to identify all sources of nonpoint and point contributors to fecal contamination of most watersheds. In this study, we used deep sequencing of sediment and water DNA to identify pathogenic bacterial sequences, including those not traditionally monitored by microbial source tracking and correlate their abundances to specific sources of contaminants. Highest sources of potential pathogens were found in urban runoff water, followed by agricultural runoff sediment and by Prado Park sediment. It was interesting to note that the number of bacteria was very high from the natural site, but the percent of potential pathogens was very low. This research will directly benefit water quality managers, water utility agencies, EPA, and the public.

Technical Abstract: Current microbial source tracking (MST) methods for water depend on testing for fecal indicator bacterial counts or specific marker gene sequences to identify fecal contamination where potential human pathogenic bacteria could be present. In this study, we applied 454 high-throughput pyrosequencing to identify bacterial pathogen DNA sequences, including those not traditionally monitored by MST and correlated their abundances to specific sources of contamination such as urban runoff and agricultural runoff from concentrated animal feeding operations (CAFOs), recreation park area, waste-water treatment plants, and natural sites with little or no human activities. Samples for pyrosequencing were surface water, and sediment collected from 19 sites. A total of 12,959 16S rRNA gene sequences with average length of less than or equal to 400 bp were obtained, and were assigned to corresponding taxonomic ranks using ribosomal database project (RDP), Classifier and Greengenes databases. The percent of total potential pathogens were highest in urban runoff water (7.94%), agricultural runoff sediment (6.52%), and Prado Park sediment (6.00%), respectively. Although the numbers of DNA sequence tags from pyrosequencing were very high for the natural site, corresponding percent potential pathogens were very low (3.78–4.08%). Most of the potential pathogenic bacterial sequences identified were from three major phyla, namely, Proteobacteria, Bacteroidetes, and Firmicutes. The use of deep sequencing may provide improved and faster methods for the identification of pathogen sources in most watersheds so that better risk assessment methods may be developed to enhance public health.